The warm recycling technology has been increasingly used in many countries due to the environmental and financial benefits. In this study, the rheological and fatigue performance evolutions of warm-mix recycled asphalt materials during the secondary service period were evaluated in two scales, mixture and fine aggregate matrix (FAM). A laboratory simulation method was proposed to produce warm-mix recycled asphalt binders with various long-term aging levels for the mixture and FAM tests. The dynamic shear rheometer temperature and frequency sweep test and time sweep test were conducted to characterize the rheological and fatigue behavior of FAMs, respectively. The rheological and fatigue properties of asphalt mixtures were measured by the dynamic modulus test and semi-circular bending test, respectively. Effects of aging levels and recycling plans on different pavement performance were investigated. Performance correlations between the mixture and FAM were finally investigated by the statistical method. It is found that the secondary long-term aging causes the continuous increase in the stiffness and decrease in the viscoelasticity level in each material scale, indicating the improvement of the rutting resistance and the reduction of the fatigue resistance. The warm mix asphalt technology plays a positive role in the fatigue performance with a loss of the rutting resistance. Using the styrene butadiene rubber latex can improve different pavement performance within the whole time-temperature domain. Good performance correlations between the mixture and FAM are developed, indicating that the FAM may be the critical material scale for evaluating the rheological and fatigue performance of warm-mix recycled asphalt pavements.

Due to the existence of uncertainties and the unknown time variant environmental disturbances for ship course nonlinear control system, the ship course adaptive neural network robust course-keeping controller is designed by combining the backstepping technique. The neural networks (NNs) are employed for the compensating of the nonlinear term of the nonlinear ship course-keeping control system. The designed adaptive laws are designed to estimate the weights of NNs and the bounds of unknown environmental disturbances. The first order commander are introduced to solve the problem of repeating differential operations in the traditional backstepping design method, which let the designed controller easier to implement in navigation practice and structure simplicity. Theoretically, it indicates that the proposed controller can track the setting course in arbitrary expected accuracy, while keeping all control signals in the ship course control closed-loop system are uniformly ultimately bounded. Finally, the training ship of Dalian Maritime University is taken for example; simulation results illustrated the effectiveness and the robustness of the proposed controller.

A robust adaptive control approach is proposed for underactuated surface ship linear path-tracking control system based on the backstepping control method and Lyapunov stability theory. By employing T-S fuzzy system to approximate nonlinear uncertainties of the control system, the proposed scheme is developed by combining “dynamic surface control” (DSC) and “minimal learning parameter” (MLP) techniques. The substantial problems of “explosion of complexity” and “dimension curse” existed in the traditional backstepping technique are circumvented, and it is convenient to implement in applications. In addition, an auxiliary system is developed to deal with the effect of input saturation constraints. The control algorithm avoids the singularity problem of controller and guarantees the stability of the closed-loop system. The tracking error converges to an arbitrarily small neighborhood. Finally, MATLAB simulation results are given from an application case of Dalian Maritime University training ship to demonstrate the effectiveness of the proposed scheme.